Small RNAseq: Differential Expression Analysis

Environment Setup

salloc -N 1 --exclusive -p amd -t 8:00:00
conda env create -f conda-env.yml
conda activate smallrna

Downloading datasets

Raw data

Raw data was downloaded from the sequencing facility using the secure link, with wget command. The downloaded files were checked for md5sum and compared against list of files expected as per the input samples provided.

wget https://oc1.rnet.missouri.edu/xyxz
# link masked 
# GEO link will be included later
# merge files of same samples (technical replicates)
paste <(ls *_L001_R1_001.fastq.gz) <(ls *_L002_R1_001.fastq.gz) | \
   sed 's/\t/ /g' |\
   awk '{print "cat",$1,$2" > "$1}' |\
   sed 's/_L001_R1_001.fastq.gz/.fq.gz/2' > concatenate.sh
chmod +x concatenate.sh
sh concatenate.sh

Genome/annotation

Additional files required for the analyses were downloaded from GenCode. The downloaded files are as follows:

wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M30/GRCm39.primary_assembly.genome.fa.gz
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M30/gencode.vM30.annotation.gff3.gz
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M30/gencode.vM30.annotation.gtf.gz
gunzip GRCm39.primary_assembly.genome.fa.gz
gunzip gencode.vM30.annotation.gff3.gz
gunzip gencode.vM30.annotation.gtf.gz

FastQC (before processing)

for fq in *.fq.gz; do
  fastqc --threads $SLURM_JOB_CPUS_PER_NODE $fq;
done
mkdir -p fastqc_pre
mv *.zip *.html fastqc_pre/

Mapping

To index the genome, following command was run (in an interactive session).

fastaGenome="GRCm39.genome.fa"
gtf="gencode.vM30.annotation.gtf"
STAR --runThreadN $SLURM_JOB_CPUS_PER_NODE \
     --runMode genomeGenerate \
     --genomeDir $(pwd) \
     --genomeFastaFiles $fastaGenome \
     --sjdbGTFfile $gtf \
     --sjdbOverhang 1

Each fastq file was mapped to the indexed genome as using runSTAR_map.sh script shown below:

#!/bin/bash
read1=$1
out=$(basename ${read1%%.*})
STARgenomeDir=$(pwd)
# illumina adapter
adapterseq="AGATCGGAAGAGC"
STAR \
    --genomeDir ${STARgenomeDir} \
    --readFilesIn ${read1} \
    --outSAMunmapped Within \
    --readFilesCommand zcat \
    --outSAMtype BAM SortedByCoordinate \
    --quantMode GeneCounts \
    --outFilterMultimapNmax 20 \
    --clip3pAdapterSeq ${adapterseq} \
    --clip3pAdapterMMp 0.1 \
    --outFilterMismatchNoverLmax 0.03 \
    --outFilterScoreMinOverLread 0 \
    --outFilterMatchNminOverLread 0 \
    --outFilterMatchNmin 16 \
    --outFileNamePrefix ${out} \
    --alignSJDBoverhangMin 1000 \ 
    --alignIntronMax 1 \
    --runThreadN ${SLURM_JOB_CPUS_PER_NODE} \
    --genomeLoad LoadAndKeep \
    --limitBAMsortRAM 30000000000 \
    --outSAMheaderHD "@HD VN:1.4 SO:coordinate"

Mapping was run with a simple loop:

for fq in *.fq.gz; do
  runSTAR_map.sh $fq;
done

Counting Stats

Counts generated by STAR with option --quantMode GeneCounts were parsed to generate summary stats as well as to extract annotated small RNA feature counts.

mkdir -p counts_files
# copy counts for each sample
cp *ReadsPerGene.out.tab counts_files/
cd counts_files
# merge counts
join_files.sh *ReadsPerGene.out.tab |\
   sed 's/ReadsPerGene.out.tab//g' |\
   grep -v "^N_" > counts_star.tsv
# merge stats
join_files.sh *ReadsPerGene.out.tab |\
   sed 's/ReadsPerGene.out.tab//g' |\
   head -n 1 > summary_star.tsv
join_files.sh *ReadsPerGene.out.tab |\
   sed 's/ReadsPerGene.out.tab//g' |\
   grep "^N_" >> summary_star.tsv
# parse GTF to extact gene.id and its biotype:
gtf=gencode.vM30.annotation.gtf
awk 'BEGIN{OFS=FS="\t"} $3=="gene" {split($9,a,";"); print a[1],a[2]}' ${gtf} |\
   awk '{print $4"\t"$2}' |\
   sed 's/"//g' > GeneType_GeneID.tsv
cut -f 1 GeneType_GeneID.tsv | sort |uniq > features.txt

The information for biotype as provided by the gencodegenes were used for categorizing biotype.

The smallRNA group consists of following biotype:

miRNA
misc_RNA
scRNA
snRNA
snoRNA
sRNA
scaRNA

The full table is as follows:

library(knitr)
setwd("/work/LAS/geetu-lab/arnstrm/mouse.trophoblast.smallRNAseq")
file1="assets/GeneType_Group.tsv"
info <-
  read.csv(
    file1,
    header = TRUE,
    sep = "\t",
    stringsAsFactors = TRUE
  )
kable(info, caption = "Table 1: biotype and its groupings")

Table 1: biotype and its groupings
biotype	group
protein_coding	coding_genes
pseudogene	pseudogenes
TR_C_gene	Ig_genes
TR_D_gene	Ig_genes
TR_J_gene	Ig_genes
TR_V_gene	Ig_genes
IG_C_gene	Ig_genes
IG_D_gene	Ig_genes
IG_J_gene	Ig_genes
IG_LV_gene	Ig_genes
IG_V_gene	Ig_genes
TR_J_pseudogene	pseudogenes
TR_V_pseudogene	pseudogenes
IG_C_pseudogene	pseudogenes
IG_D_pseudogene	pseudogenes
IG_pseudogene	pseudogenes
IG_V_pseudogene	pseudogenes
lncRNA	long_non_conding_RNA
miRNA	non_conding_RNA
misc_RNA	non_conding_RNA
ribozyme	non_conding_RNA
rRNA	non_conding_RNA
scaRNA	non_conding_RNA
scRNA	non_conding_RNA
snoRNA	non_conding_RNA
snRNA	non_conding_RNA
sRNA	non_conding_RNA
Mt_rRNA	non_conding_RNA
Mt_tRNA	non_conding_RNA
processed_pseudogene	pseudogenes
unprocessed_pseudogene	pseudogenes
translated_unprocessed_pseudogene	pseudogenes
transcribed_processed_pseudogene	pseudogenes
transcribed_unitary_pseudogene	pseudogenes
transcribed_unprocessed_pseudogene	pseudogenes
unitary_pseudogene	pseudogenes
TEC	unconfirmed_genes

A samples table (samples.tsv) categorizing samples to its condition were also generated:

file2="assets/samples.tsv"
samples <-
  read.csv(
    file2,
    header = TRUE,
    sep = "\t",
    stringsAsFactors = TRUE
  )
kable(samples, caption = "Table 2: Samples in the study")

Table 2: Samples in the study
Sample	Group
Dif_D6_1_S4	Diff
Dif_D6_2_S3	Diff
Dif_D6_3_S2	Diff
Dif_D6_4_S1	Diff
Undif_D2_1_S8	Undf
Undif_D2_2_S7	Undf
Undif_D2_3_S6	Undf
Undif_D2_4_S5	Undf

This information was then merged withe counts table to generate QC plots:

awk 'BEGIN{OFS=FS="\t"}FNR==NR{a[$1]=$2;next}{ print $2,$1,a[$1]}' \
    GeneType_Group.tsv GeneType_GeneID.tsv > GeneID_GeneType_Group.tsv

awk 'BEGIN{OFS=FS="\t"}FNR==NR{a[$1]=$2"\t"$3;next}{print $1,a[$1],$0}' \
    GeneID_GeneType_Group.tsv counts_star.tsv |\
    cut -f 1-3,5- > processed_counts_star.tsv

Plotting the mapping summary and count statistics for various biotypes:

library(scales)
library(tidyverse)
library(plotly)

setwd("/work/LAS/geetu-lab/arnstrm/mouse.trophoblast.smallRNAseq")
file1="assets/processed_counts_star.tsv"
file2="assets/summary_stats_star.tsv"
counts <-
  read.csv(
    file1,
    sep = "\t",
    stringsAsFactors = TRUE
  )
subread <-
  read.csv(
    file2,
    sep = "\t",
    stringsAsFactors = TRUE
  )
# convert long format
counts.long <- gather(counts, Sample, Count, Dif_D6_1_S4:Undif_D2_4_S5, factor_key=TRUE)
subread.long <- gather(subread, Sample,  Count, Dif_D6_1_S4:Undif_D2_4_S5, factor_key=TRUE)
# organize
counts.long$Group <-
  factor(
    counts.long$Group,
    levels = c(
      "coding_genes",
      "non_conding_RNA",
      "long_non_conding_RNA",
      "pseudogenes",
      "unconfirmed_genes",
      "Ig_genes"
    )
  )

subread.long$Assignments <-
  factor(
    subread.long$Assignments,
    levels = c(
      "N_input",
      "N_unmapped",
      "N_multimapping",
      "N_unique",
      "N_ambiguous",
      "N_noFeature"
    )
  )

ggplot(subread.long, aes(x = Assignments, y = Count, fill = Assignments)) +
  geom_bar(stat = 'identity') +
  labs(x = "Subread assingments", y = "reads") + theme_minimal() +
  scale_y_continuous(labels = label_comma()) +
  theme(
    axis.text.x = element_text(
      angle = 45,
      vjust = 1,
      hjust = 1,
      size = 12
      ),
    strip.text = element_text(
      face = "bold",
      color = "gray35",
      hjust = 0,
      size = 10
    ),
    strip.background = element_rect(fill = "white", linetype = "blank"),
    legend.position = "none"
  ) +
  facet_wrap("Sample", scales = "free_y", ncol = 4)

Figure 1: STAR read mapping and feature assignment. Here, N_input is total input reads, N_unmapped is reads that were either too short to map after adapter removal or had higher mismatch rate to place reliably on the genome, N_multimapping is reads mapped to multiple loci, N_unique is reads mapped to unique loci. A subset of N_unique reads that were unable to clearly assign to a feature or assign any feature at all are grouped as N_ambigious or N_noFeature, respectively

g <- ggplot(counts.long, aes(x = Group, y = Count, fill = Group)) +
  geom_bar(stat = 'sum') +
  labs(x = "biotype", y = "read counts") + theme_minimal() +
  scale_y_continuous(labels = label_comma()) +
  theme(
    axis.text.x = element_text(
      angle = 45,
      vjust = 1,
      hjust = 1,
      size = 12
    ),
    strip.text = element_text(
      face = "bold",
      color = "gray35",
      hjust = 0,
      size = 10
    ),
    strip.background = element_rect(fill = "white", linetype = "blank"),
    legend.position = "none"
  ) +
  facet_wrap("Sample", scales = "free_y", ncol = 4)
#ggplotly(g)
g

Figure 2: Features with read counts

counts.nc <- filter(counts.long, Group %in% "non_conding_RNA" )
counts.nc$GeneType <-
  factor(
    counts.nc$GeneType,
    levels = c(
      "miRNA",
      "misc_RNA",
      "snoRNA",
      "snRNA",
      "sRNA",
      "scRNA",
      "scaRNA",
      "Mt_tRNA",
      "Mt_rRNA",
      "rRNA",
      "ribozyme"
    )
  )

g <- ggplot(counts.nc, aes(x = GeneType, y = Count, fill = GeneType)) +
  geom_bar(stat = 'sum') +
  labs(x = "biotype", y = "read counts") + theme_minimal() +
  scale_y_continuous(labels = label_comma()) +
  theme(
    axis.text.x = element_text(
      angle = 45,
      vjust = 1,
      hjust = 1,
      size = 12
    ),
    strip.text = element_text(
      face = "bold",
      color = "gray35",
      hjust = 0,
      size = 10
    ),
    strip.background = element_rect(fill = "white", linetype = "blank"),
    legend.position = "none"
  ) +
  facet_wrap("Sample", scales = "free_y", ncol = 4)
#ggplotly(g)
g

Figure 3: non-coding biotype read counts

subset the counts file to select only smallRNA genes

snrna <- c('miRNA',
           'misc_RNA',
           'scRNA',
           'snRNA',
           'snoRNA',
           'sRNA',
           'scaRNA')
cts <- filter(counts, GeneType %in% snrna) %>%
  select(Geneid, Dif_D6_1_S4:Undif_D2_4_S5)
write_delim(cts, file = "assets/noncoding_counts_star.tsv", delim = "\t")

This noncoding_counts_star.tsv and samples.tsv file will be used for DESeq2 analyses.

DESeq2

For the next steps, we used DESeq2 for performing the DE analyses. Results were visualized as volcano plots and tables were exported to excel.

Load packages

setwd("/work/LAS/geetu-lab/arnstrm/mouse.trophoblast.smallRNAseq")
library(DESeq2)
library(RColorBrewer)
library(pheatmap)
library(genefilter)
library(ggrepel)

Import counts and sample metadata

The counts data and its associated metadata (coldata) are imported for analyses.

counts = 'assets/noncoding_counts_star.tsv'
groupFile = 'assets/samples.tsv'
coldata <-
  read.csv(
    groupFile,
    row.names = 1,
    sep = "\t",
    stringsAsFactors = TRUE
  )
cts <- as.matrix(read.csv(counts, sep = "\t", row.names = "Geneid"))

Reorder columns of cts according to coldata rows. Check if samples in both files match.

colnames(cts)
#> [1] "Dif_D6_1_S4"   "Dif_D6_2_S3"   "Dif_D6_3_S2"   "Dif_D6_4_S1"  
#> [5] "Undif_D2_1_S8" "Undif_D2_2_S7" "Undif_D2_3_S6" "Undif_D2_4_S5"
all(rownames(coldata) %in% colnames(cts))
#> [1] TRUE
cts <- cts[, rownames(coldata)]

Normalize

The batch corrected read counts are then used for running DESeq2 analyses

dds <- DESeqDataSetFromMatrix(countData = cts,
                              colData = coldata,
                              design = ~ Group)
vsd <- vst(dds, blind = FALSE, nsub =500)
keep <- rowSums(counts(dds)) >= 5
dds <- dds[keep, ]
dds <- DESeq(dds)
dds
#> class: DESeqDataSet 
#> dim: 1266 8 
#> metadata(1): version
#> assays(4): counts mu H cooks
#> rownames(1266): ENSMUSG00000119106.1 ENSMUSG00000119589.1 ...
#>   ENSMUSG00000065444.3 ENSMUSG00000077869.3
#> rowData names(22): baseMean baseVar ... deviance maxCooks
#> colnames(8): Dif_D6_1_S4 Dif_D6_2_S3 ... Undif_D2_3_S6 Undif_D2_4_S5
#> colData names(2): Group sizeFactor

vst <- assay(vst(dds, blind = FALSE, nsub = 500))
vsd <- vst(dds, blind = FALSE, nsub = 500)
pcaData <-
  plotPCA(vsd,
          intgroup = "Group",
          returnData = TRUE)
percentVar <- round(100 * attr(pcaData, "percentVar"))

PCA plot for QC

PCA plot for the dataset that includes all libraries.

rv <- rowVars(assay(vsd))
select <-
  order(rv, decreasing = TRUE)[seq_len(min(500, length(rv)))]
pca <- prcomp(t(assay(vsd)[select, ]))
percentVar <- pca$sdev ^ 2 / sum(pca$sdev ^ 2)
intgroup = "Group"
intgroup.df <- as.data.frame(colData(vsd)[, intgroup, drop = FALSE])
group <- if (length(intgroup) == 1) {
  factor(apply(intgroup.df, 1, paste, collapse = " : "))
}
d <- data.frame(
  PC1 = pca$x[, 1],
  PC2 = pca$x[, 2],
  intgroup.df,
  name = colnames(vsd)
)

plot PCA for components 1 and 2

g <- ggplot(d, aes(PC1, PC2, color = Group)) +
  scale_shape_manual(values = 1:8) +
  theme_bw() +
  theme(legend.title = element_blank()) +
  geom_point(size = 2, stroke = 2) +
  xlab(paste("PC1", round(percentVar[1] * 100, 2), "% variance")) +
  ylab(paste("PC2", round(percentVar[2] * 100, 2), "% variance"))
ggplotly(g)

Figure 4: PCA plot for the first 2 principal components

#g

Sample distance for QC

sampleDists <- dist(t(assay(vsd)))
sampleDistMatrix <- as.matrix( sampleDists )
rownames(sampleDistMatrix) <- colnames(vsd)
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
pheatmap(sampleDistMatrix,
         clustering_distance_rows = sampleDists,
         clustering_distance_cols = sampleDists,
         col = colors)

Figure 5: Euclidean distance between samples

Set contrasts and find DE genes

resultsNames(dds)
#> [1] "Intercept"          "Group_Undf_vs_Diff"
res.UndfvsDiff <- results(dds, contrast = c("Group", "Undf", "Diff"))
table(res.UndfvsDiff$padj < 0.05)
#> 
#> FALSE  TRUE 
#>   579   294
res.UndfvsDiff <- res.UndfvsDiff[order(res.UndfvsDiff$padj),]
res.UndfvsDiffdata <-
  merge(
    as.data.frame(res.UndfvsDiff),
    as.data.frame(counts(dds, normalized = TRUE)),
    by = "row.names",
    sort = FALSE
  )
names(res.UndfvsDiffdata)[1] <- "Gene"
write_delim(res.UndfvsDiffdata, file = "DESeq2results-UndfvsDiff_fc.tsv", delim = "\t")

Volcano plots

mart <-
  read.csv(
    "assets/mart_export.txt",
    sep = "\t",
    stringsAsFactors = TRUE,
    header = TRUE
  ) #this object was obtained from Ensembl as we illustrated in "Creating gene lists"

volcanoPlots2 <-
  function(res.se,
           string,
           first,
           second,
           color1,
           color2,
           color3,
           ChartTitle) {
    res.se <- res.se[order(res.se$padj), ]
    res.se <-
      rownames_to_column(as.data.frame(res.se[order(res.se$padj), ]))
    names(res.se)[1] <- "Gene"
    res.data <-
      merge(res.se,
            mart,
            by.x = "Gene",
            by.y = "geneid.version")
    res.data <- res.data %>% mutate_all(na_if, "")
    res.data <- res.data %>% mutate_all(na_if, " ")
    res.data <-
      res.data %>% mutate(gene_symbol = coalesce(gene.symbol, Gene))
    res.data$diffexpressed <- "other.genes"
    res.data$diffexpressed[res.data$log2FoldChange >= 1 &
                             res.data$padj <= 0.05] <-
      paste("Higher expression in", first)
    res.data$diffexpressed[res.data$log2FoldChange <= -1 &
                             res.data$padj <= 0.05] <-
      paste("Higher expression in", second)
    res.data$delabel <- ""
    res.data$delabel[res.data$log2FoldChange >= 1
                     & res.data$padj <= 0.05
                     &
                       !is.na(res.data$padj)] <-
      res.data$gene_symbol[res.data$log2FoldChange >= 1
                           &
                             res.data$padj <= 0.05
                           &
                             !is.na(res.data$padj)]
    res.data$delabel[res.data$log2FoldChange <= -1
                     & res.data$padj <= 0.05
                     &
                       !is.na(res.data$padj)] <-
      res.data$gene_symbol[res.data$log2FoldChange <= -1
                           &
                             res.data$padj <= 0.05
                           &
                             !is.na(res.data$padj)]
    ggplot(res.data,
             aes(
               x = log2FoldChange,
               y = -log10(padj),
               col = diffexpressed,
               label = delabel
             )) +
      geom_point(alpha = 0.5) +
      xlim(-20, 20) +
      theme_classic() +
      scale_color_manual(name = "Expression", values = c(color1, color2, color3)) +
      # geom_text_repel(
      #   data = subset(res.data, padj <= 0.05),
      #   max.overlaps  = 15,
      #   show.legend = F,
      #   min.segment.length = Inf,
      #   seed = 42,
      #   box.padding = 0.5
      # ) +
      ggtitle(ChartTitle) +
      xlab(paste("log2 fold change")) +
      ylab("-log10 pvalue (adjusted)") +
      theme(legend.text.align = 0)
}

g <- volcanoPlots2(
  res.UndfvsDiff,
  "UndfvsDiff",
  "Undf",
  "Diff",
  "green",
  "blue",
  "grey",
  ChartTitle = "Undifferentiated vs. Differentiated"
)
ggplotly(g)

Figure 6: Volcano plot showing genes overexpressed in undifferentiated and differentiated states.

#g

Heatmap

Heatmap for the top 30 variable genes:

topVarGenes <- head(order(rowVars(assay(vsd)), decreasing = TRUE), 30)
mat  <- assay(vsd)[ topVarGenes, ]
mat  <- mat - rowMeans(mat)
mat2 <-    merge(mat,
          mart,
          by.x = 'row.names',
          by.y = "geneid.version")
rownames(mat2) <- mat2[,10]
mat2 <- mat2[2:9]
heat_colors <- brewer.pal(9, "YlOrRd")
g <- pheatmap(
      mat2,
      color = heat_colors,
      main = "Top 30 variable small RNA genes",
      cluster_rows = T,
      cluster_cols  = T,
      show_rownames = T,
      border_color = NA,
      fontsize = 10,
      scale = "row",
      fontsize_row = 10
    )
    g

Figure 7: Heat map for top 30 variable small RNA genes

MultiQC report:

MultiQC report is available at this link

Session Information

sessionInfo()
#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] ggrepel_0.9.1               genefilter_1.78.0          
#>  [3] pheatmap_1.0.12             RColorBrewer_1.1-3         
#>  [5] DESeq2_1.36.0               SummarizedExperiment_1.26.1
#>  [7] Biobase_2.56.0              MatrixGenerics_1.8.1       
#>  [9] matrixStats_0.62.0          GenomicRanges_1.48.0       
#> [11] GenomeInfoDb_1.32.2         IRanges_2.30.0             
#> [13] S4Vectors_0.34.0            BiocGenerics_0.42.0        
#> [15] plotly_4.10.0               forcats_0.5.1              
#> [17] stringr_1.4.0               dplyr_1.0.9                
#> [19] purrr_0.3.4                 readr_2.1.2                
#> [21] tidyr_1.2.0                 tibble_3.1.8               
#> [23] ggplot2_3.3.6               tidyverse_1.3.2            
#> [25] scales_1.2.0                knitr_1.39                 
#> 
#> loaded via a namespace (and not attached):
#>  [1] googledrive_2.0.0      colorspace_2.0-3       ellipsis_0.3.2        
#>  [4] XVector_0.36.0         fs_1.5.2               rstudioapi_0.13       
#>  [7] farver_2.1.1           bit64_4.0.5            AnnotationDbi_1.58.0  
#> [10] fansi_1.0.3            lubridate_1.8.0        xml2_1.3.3            
#> [13] codetools_0.2-18       splines_4.2.1          cachem_1.0.6          
#> [16] geneplotter_1.74.0     jsonlite_1.8.0         broom_1.0.0           
#> [19] annotate_1.74.0        dbplyr_2.2.1           png_0.1-7             
#> [22] compiler_4.2.1         httr_1.4.3             backports_1.4.1       
#> [25] assertthat_0.2.1       Matrix_1.4-1           fastmap_1.1.0         
#> [28] lazyeval_0.2.2         gargle_1.2.0           cli_3.3.0             
#> [31] htmltools_0.5.3        tools_4.2.1            gtable_0.3.0          
#> [34] glue_1.6.2             GenomeInfoDbData_1.2.8 Rcpp_1.0.9            
#> [37] cellranger_1.1.0       jquerylib_0.1.4        vctrs_0.4.1           
#> [40] Biostrings_2.64.0      crosstalk_1.2.0        xfun_0.31             
#> [43] rvest_1.0.2            lifecycle_1.0.1        XML_3.99-0.10         
#> [46] googlesheets4_1.0.0    zlibbioc_1.42.0        vroom_1.5.7           
#> [49] hms_1.1.1              parallel_4.2.1         yaml_2.3.5            
#> [52] memoise_2.0.1          sass_0.4.2             stringi_1.7.8         
#> [55] RSQLite_2.2.15         highr_0.9              BiocParallel_1.30.3   
#> [58] rlang_1.0.4            pkgconfig_2.0.3        bitops_1.0-7          
#> [61] evaluate_0.15          lattice_0.20-45        htmlwidgets_1.5.4     
#> [64] labeling_0.4.2         bit_4.0.4              tidyselect_1.1.2      
#> [67] magrittr_2.0.3         bookdown_0.27          R6_2.5.1              
#> [70] generics_0.1.3         DelayedArray_0.22.0    DBI_1.1.3             
#> [73] pillar_1.8.0           haven_2.5.0            withr_2.5.0           
#> [76] survival_3.3-1         KEGGREST_1.36.3        RCurl_1.98-1.8        
#> [79] modelr_0.1.8           crayon_1.5.1           utf8_1.2.2            
#> [82] tzdb_0.3.0             rmarkdown_2.14         locfit_1.5-9.6        
#> [85] grid_4.2.1             readxl_1.4.0           data.table_1.14.2     
#> [88] blob_1.2.3             rmdformats_1.0.4       reprex_2.0.1          
#> [91] digest_0.6.29          xtable_1.8-4           munsell_0.5.0         
#> [94] viridisLite_0.4.0      bslib_0.4.0